Overview

Dataset statistics

Number of variables21
Number of observations12651
Missing cells0
Missing cells (%)0.0%
Duplicate rows22
Duplicate rows (%)0.2%
Total size in memory1.8 MiB
Average record size in memory147.0 B

Variable types

Numeric11
Categorical10

Alerts

Dataset has 22 (0.2%) duplicate rowsDuplicates
Bilirubin is highly overall correlated with CopperHigh correlation
Copper is highly overall correlated with BilirubinHigh correlation
Edema_N is highly overall correlated with Edema_S and 1 other fieldsHigh correlation
Edema_S is highly overall correlated with Edema_NHigh correlation
Edema_Y is highly overall correlated with Edema_NHigh correlation
is_male is highly imbalanced (79.9%)Imbalance
Ascites is highly imbalanced (92.5%)Imbalance
Edema_N is highly imbalanced (79.9%)Imbalance
Edema_S is highly imbalanced (85.9%)Imbalance
Edema_Y is highly imbalanced (91.0%)Imbalance
Status is uniformly distributedUniform

Reproduction

Analysis started2024-01-03 08:06:16.113987
Analysis finished2024-01-03 08:06:29.884559
Duration13.77 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

N_years
Real number (ℝ)

Distinct6524
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2591544
Minimum0.11232877
Maximum13.136986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:29.977533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.11232877
5-th percentile1.9405427
Q13.3808219
median4.8254377
Q36.7616438
95-th percentile10.407264
Maximum13.136986
Range13.024658
Interquartile range (IQR)3.3808219

Descriptive statistics

Standard deviation2.5687181
Coefficient of variation (CV)0.48842797
Kurtosis-0.11970502
Mean5.2591544
Median Absolute Deviation (MAD)1.7042858
Skewness0.62660198
Sum66533.562
Variance6.5983127
MonotonicityNot monotonic
2024-01-03T11:36:30.090434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.761643836 154
 
1.2%
2.468493151 107
 
0.8%
6.139726027 100
 
0.8%
3.331506849 93
 
0.7%
2.106849315 86
 
0.7%
2.476712329 83
 
0.7%
3.928767123 68
 
0.5%
9.438356164 67
 
0.5%
2.969863014 65
 
0.5%
6.780821918 62
 
0.5%
Other values (6514) 11766
93.0%
ValueCountFrequency (%)
0.1123287671 2
 
< 0.1%
0.1397260274 5
 
< 0.1%
0.1945205479 2
 
< 0.1%
0.2109589041 1
 
< 0.1%
0.2565826507 1
 
< 0.1%
0.2933346337 1
 
< 0.1%
0.301369863 17
0.1%
0.3358356974 1
 
< 0.1%
0.3429842032 1
 
< 0.1%
0.3502873616 1
 
< 0.1%
ValueCountFrequency (%)
13.1369863 6
 
< 0.1%
12.8167842 1
 
< 0.1%
12.48219178 23
0.2%
12.39178082 7
 
0.1%
12.35342466 16
0.1%
12.32876712 13
0.1%
12.29359753 1
 
< 0.1%
12.23835616 8
 
0.1%
12.22838379 1
 
< 0.1%
12.21643836 12
0.1%

Age
Real number (ℝ)

Distinct6431
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.416377
Minimum26.29589
Maximum78.493151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:30.196437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.29589
5-th percentile34.085263
Q141.008364
median47.937604
Q355.448014
95-th percentile63.673973
Maximum78.493151
Range52.19726
Interquartile range (IQR)14.439651

Descriptive statistics

Standard deviation9.2681745
Coefficient of variation (CV)0.19142643
Kurtosis-0.50304814
Mean48.416377
Median Absolute Deviation (MAD)7.0705293
Skewness0.26644654
Sum612515.59
Variance85.899058
MonotonicityNot monotonic
2024-01-03T11:36:30.299978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.21369863 129
 
1.0%
40.28767123 118
 
0.9%
36.51780822 113
 
0.9%
40.92876712 102
 
0.8%
46.41369863 100
 
0.8%
41.18082192 91
 
0.7%
61.3369863 73
 
0.6%
52.21917808 72
 
0.6%
56.66849315 65
 
0.5%
53.34246575 57
 
0.5%
Other values (6421) 11731
92.7%
ValueCountFrequency (%)
26.29589041 13
0.1%
28.90410959 16
0.1%
29.1247559 1
 
< 0.1%
29.1593806 1
 
< 0.1%
29.23091088 1
 
< 0.1%
29.33548947 1
 
< 0.1%
29.46141509 1
 
< 0.1%
29.50726448 1
 
< 0.1%
29.57534247 5
 
< 0.1%
29.72796708 1
 
< 0.1%
ValueCountFrequency (%)
78.49315068 6
 
< 0.1%
77.39047862 1
 
< 0.1%
77.17238265 1
 
< 0.1%
76.86820419 1
 
< 0.1%
76.76164384 3
 
< 0.1%
76.16647186 1
 
< 0.1%
75.40712541 1
 
< 0.1%
75.0630137 23
0.2%
74.6407131 1
 
< 0.1%
74.57534247 18
0.1%

is_male
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size741.4 KiB
0.0
12255 
1.0
 
396

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37953
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 12255
96.9%
1.0 396
 
3.1%

Length

2024-01-03T11:36:30.384709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:30.446572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12255
96.9%
1.0 396
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 24906
65.6%
. 12651
33.3%
1 396
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25302
66.7%
Other Punctuation 12651
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24906
98.4%
1 396
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 12651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24906
65.6%
. 12651
33.3%
1 396
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24906
65.6%
. 12651
33.3%
1 396
 
1.0%

Ascites
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
0
12535 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12535
99.1%
1 116
 
0.9%

Length

2024-01-03T11:36:30.512140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:30.625517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12535
99.1%
1 116
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 12535
99.1%
1 116
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12535
99.1%
1 116
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12535
99.1%
1 116
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12535
99.1%
1 116
 
0.9%

Hepatomegaly
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
1
7646 
0
5005 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7646
60.4%
0 5005
39.6%

Length

2024-01-03T11:36:30.796668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:30.933181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7646
60.4%
0 5005
39.6%

Most occurring characters

ValueCountFrequency (%)
1 7646
60.4%
0 5005
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7646
60.4%
0 5005
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7646
60.4%
0 5005
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7646
60.4%
0 5005
39.6%

Spiders
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
0
9596 
1
3055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9596
75.9%
1 3055
 
24.1%

Length

2024-01-03T11:36:31.024047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:31.094276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9596
75.9%
1 3055
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 9596
75.9%
1 3055
 
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9596
75.9%
1 3055
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9596
75.9%
1 3055
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9596
75.9%
1 3055
 
24.1%

Bilirubin
Real number (ℝ)

HIGH CORRELATION 

Distinct5883
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9515906
Minimum0.3
Maximum6.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:31.173776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.84657995
median1.4514639
Q33
95-th percentile4.545268
Maximum6.4
Range6.1
Interquartile range (IQR)2.1534201

Descriptive statistics

Standard deviation1.3299612
Coefficient of variation (CV)0.68147548
Kurtosis0.44621523
Mean1.9515906
Median Absolute Deviation (MAD)0.77798416
Skewness0.99161159
Sum24689.573
Variance1.7687967
MonotonicityNot monotonic
2024-01-03T11:36:31.266583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 719
 
5.7%
0.7 590
 
4.7%
0.5 584
 
4.6%
0.8 550
 
4.3%
0.9 513
 
4.1%
1.1 452
 
3.6%
1.3 450
 
3.6%
1 276
 
2.2%
3.2 273
 
2.2%
3.4 206
 
1.6%
Other values (5873) 8038
63.5%
ValueCountFrequency (%)
0.3 51
 
0.4%
0.3264924957 1
 
< 0.1%
0.3856655338 1
 
< 0.1%
0.3962994666 1
 
< 0.1%
0.4 176
1.4%
0.4056666939 1
 
< 0.1%
0.406503731 1
 
< 0.1%
0.4067682272 1
 
< 0.1%
0.4076799932 1
 
< 0.1%
0.4098887812 1
 
< 0.1%
ValueCountFrequency (%)
6.4 55
0.4%
6.396264854 1
 
< 0.1%
6.383552051 1
 
< 0.1%
6.369475515 1
 
< 0.1%
6.355816062 1
 
< 0.1%
6.351186748 1
 
< 0.1%
6.335692967 1
 
< 0.1%
6.327429434 1
 
< 0.1%
6.322753681 1
 
< 0.1%
6.31603519 1
 
< 0.1%

Cholesterol
Real number (ℝ)

Distinct5615
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320.64242
Minimum120
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:31.359357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile205
Q1253
median303
Q3374
95-th percentile495.02464
Maximum588
Range468
Interquartile range (IQR)121

Descriptive statistics

Standard deviation89.757772
Coefficient of variation (CV)0.27993106
Kurtosis0.12512306
Mean320.64242
Median Absolute Deviation (MAD)55.798092
Skewness0.75961063
Sum4056447.2
Variance8056.4577
MonotonicityNot monotonic
2024-01-03T11:36:31.453033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316 252
 
2.0%
348 159
 
1.3%
248 156
 
1.2%
339 154
 
1.2%
528 145
 
1.1%
263 133
 
1.1%
298 129
 
1.0%
232 120
 
0.9%
396 118
 
0.9%
450 114
 
0.9%
Other values (5605) 11171
88.3%
ValueCountFrequency (%)
120 8
 
0.1%
127 30
0.2%
132 34
0.3%
134 1
 
< 0.1%
134.8036692 1
 
< 0.1%
140.4377783 1
 
< 0.1%
141.3352207 1
 
< 0.1%
145.7351726 1
 
< 0.1%
146.9403729 1
 
< 0.1%
147.8665235 1
 
< 0.1%
ValueCountFrequency (%)
588 1
 
< 0.1%
586 6
 
< 0.1%
585.925331 1
 
< 0.1%
585.2291857 1
 
< 0.1%
584.4519793 1
 
< 0.1%
584.3224204 1
 
< 0.1%
583.3434267 1
 
< 0.1%
580.9771332 1
 
< 0.1%
580.7628101 1
 
< 0.1%
578 40
0.3%

Albumin
Real number (ℝ)

Distinct6520
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5594897
Minimum2.73
Maximum4.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:31.539026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.73
5-th percentile3.0926727
Q13.36
median3.5684706
Q33.74
95-th percentile4.0649078
Maximum4.4
Range1.67
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.28325069
Coefficient of variation (CV)0.079576208
Kurtosis0.059355912
Mean3.5594897
Median Absolute Deviation (MAD)0.19009797
Skewness0.062726843
Sum45031.104
Variance0.080230953
MonotonicityNot monotonic
2024-01-03T11:36:31.635015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.35 312
 
2.5%
3.6 283
 
2.2%
3.7 263
 
2.1%
3.85 215
 
1.7%
3.77 192
 
1.5%
3.5 171
 
1.4%
3.2 166
 
1.3%
3.65 154
 
1.2%
3.61 146
 
1.2%
3.18 138
 
1.1%
Other values (6510) 10611
83.9%
ValueCountFrequency (%)
2.73 3
 
< 0.1%
2.74 5
 
< 0.1%
2.746479822 1
 
< 0.1%
2.75 45
0.4%
2.750005499 1
 
< 0.1%
2.765779037 1
 
< 0.1%
2.766645523 1
 
< 0.1%
2.768084834 1
 
< 0.1%
2.768940566 1
 
< 0.1%
2.77 1
 
< 0.1%
ValueCountFrequency (%)
4.4 14
0.1%
4.381728602 1
 
< 0.1%
4.38 19
0.2%
4.379063205 1
 
< 0.1%
4.374894481 1
 
< 0.1%
4.372396426 1
 
< 0.1%
4.367490733 1
 
< 0.1%
4.36649819 1
 
< 0.1%
4.364298933 1
 
< 0.1%
4.362204076 1
 
< 0.1%

Copper
Real number (ℝ)

HIGH CORRELATION 

Distinct5598
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.939009
Minimum4
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:31.728632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile20
Q142
median67
Q396
95-th percentile144.0955
Maximum196
Range192
Interquartile range (IQR)54

Descriptive statistics

Standard deviation38.326875
Coefficient of variation (CV)0.53276901
Kurtosis-0.093961933
Mean71.939009
Median Absolute Deviation (MAD)26.162855
Skewness0.65712891
Sum910100.41
Variance1468.9493
MonotonicityNot monotonic
2024-01-03T11:36:31.830248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121 341
 
2.7%
75 327
 
2.6%
67 281
 
2.2%
52 248
 
2.0%
44 234
 
1.8%
58 229
 
1.8%
77 224
 
1.8%
20 204
 
1.6%
39 196
 
1.5%
102 178
 
1.4%
Other values (5588) 10189
80.5%
ValueCountFrequency (%)
4 12
 
0.1%
4.420480248 1
 
< 0.1%
5 1
 
< 0.1%
6.120768774 1
 
< 0.1%
6.648427237 1
 
< 0.1%
8.025748794 1
 
< 0.1%
8.136765134 1
 
< 0.1%
9 49
0.4%
9.057872445 1
 
< 0.1%
9.05983032 1
 
< 0.1%
ValueCountFrequency (%)
196 2
 
< 0.1%
190 1
 
< 0.1%
188 31
0.2%
187.8365304 1
 
< 0.1%
186.9250637 1
 
< 0.1%
186.729402 1
 
< 0.1%
186.5916134 1
 
< 0.1%
186.1774764 1
 
< 0.1%
186 15
0.1%
185.8676702 1
 
< 0.1%

Alk_Phos
Real number (ℝ)

Distinct5372
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1303.6862
Minimum289
Maximum3336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:31.926346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum289
5-th percentile622
Q1846.16726
median1162
Q31620
95-th percentile2520
Maximum3336
Range3047
Interquartile range (IQR)773.83274

Descriptive statistics

Standard deviation606.44671
Coefficient of variation (CV)0.46517845
Kurtosis1.4299794
Mean1303.6862
Median Absolute Deviation (MAD)371
Skewness1.1997937
Sum16492934
Variance367777.61
MonotonicityNot monotonic
2024-01-03T11:36:32.015148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1345 275
 
2.2%
1162 155
 
1.2%
3336 147
 
1.2%
938 135
 
1.1%
2276 128
 
1.0%
1440 118
 
0.9%
663 111
 
0.9%
1408 108
 
0.9%
1136 107
 
0.8%
1533 90
 
0.7%
Other values (5362) 11277
89.1%
ValueCountFrequency (%)
289 44
0.3%
296.1044501 1
 
< 0.1%
302.9560298 1
 
< 0.1%
309.2518479 1
 
< 0.1%
310 9
 
0.1%
313.6607064 1
 
< 0.1%
316.1203859 1
 
< 0.1%
319.7483787 1
 
< 0.1%
321.1275685 1
 
< 0.1%
323.655056 1
 
< 0.1%
ValueCountFrequency (%)
3336 147
1.2%
3334.922441 1
 
< 0.1%
3332.733471 1
 
< 0.1%
3331.510513 1
 
< 0.1%
3331.149767 1
 
< 0.1%
3330.914915 1
 
< 0.1%
3327.097631 1
 
< 0.1%
3326.543882 1
 
< 0.1%
3325.222435 1
 
< 0.1%
3325.15926 1
 
< 0.1%

SGOT
Real number (ℝ)

Distinct5592
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.60444
Minimum26.35
Maximum227.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:32.104851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile57.157658
Q189.9
median117.69824
Q3137.95
95-th percentile185.90894
Maximum227.04
Range200.69
Interquartile range (IQR)48.05

Descriptive statistics

Standard deviation37.681866
Coefficient of variation (CV)0.32315978
Kurtosis-0.14118207
Mean116.60444
Median Absolute Deviation (MAD)24.698238
Skewness0.33655685
Sum1475162.8
Variance1419.923
MonotonicityNot monotonic
2024-01-03T11:36:32.213431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3 215
 
1.7%
137.95 214
 
1.7%
57.35 212
 
1.7%
120.9 188
 
1.5%
128.65 177
 
1.4%
97.65 169
 
1.3%
93 165
 
1.3%
66.65 162
 
1.3%
147.25 161
 
1.3%
170.5 154
 
1.2%
Other values (5582) 10834
85.6%
ValueCountFrequency (%)
26.35 6
 
< 0.1%
28.38 3
 
< 0.1%
40.6 1
 
< 0.1%
41.85 17
0.1%
43.00556478 1
 
< 0.1%
43.4 41
0.3%
43.51145398 1
 
< 0.1%
44.85377535 1
 
< 0.1%
44.91592986 1
 
< 0.1%
45 15
 
0.1%
ValueCountFrequency (%)
227.04 1
 
< 0.1%
225.1053899 1
 
< 0.1%
223.6178543 1
 
< 0.1%
222.6804644 1
 
< 0.1%
221.88 6
< 0.1%
221.768139 1
 
< 0.1%
221.65 3
< 0.1%
221.04 1
 
< 0.1%
220.8075528 1
 
< 0.1%
220.7350692 1
 
< 0.1%

Tryglicerides
Real number (ℝ)

Distinct5673
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.82242
Minimum33
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:32.314690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile58
Q184
median102.6483
Q3128
95-th percentile168
Maximum219
Range186
Interquartile range (IQR)44

Descriptive statistics

Standard deviation32.473196
Coefficient of variation (CV)0.30399233
Kurtosis0.29004162
Mean106.82242
Median Absolute Deviation (MAD)20.351703
Skewness0.64018675
Sum1351410.4
Variance1054.5085
MonotonicityNot monotonic
2024-01-03T11:36:32.411543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 313
 
2.5%
146 274
 
2.2%
118 256
 
2.0%
91 234
 
1.8%
90 221
 
1.7%
137 211
 
1.7%
68 206
 
1.6%
85 204
 
1.6%
113 188
 
1.5%
78 178
 
1.4%
Other values (5663) 10366
81.9%
ValueCountFrequency (%)
33 11
 
0.1%
33.41462783 1
 
< 0.1%
36.11058286 1
 
< 0.1%
38.34057846 1
 
< 0.1%
40.05872905 1
 
< 0.1%
43.06135588 1
 
< 0.1%
44 35
0.3%
44.78446921 1
 
< 0.1%
45.36234662 1
 
< 0.1%
45.81373079 1
 
< 0.1%
ValueCountFrequency (%)
219 7
 
0.1%
218 6
 
< 0.1%
216.9723028 1
 
< 0.1%
215.3684756 1
 
< 0.1%
214 33
0.3%
213.1506125 1
 
< 0.1%
213 17
0.1%
212.788025 1
 
< 0.1%
211.9013838 1
 
< 0.1%
210.8061147 1
 
< 0.1%

Platelets
Real number (ℝ)

Distinct5821
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.52557
Minimum62
Maximum474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:32.522750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile135.52923
Q1213
median258.11736
Q3309.9353
95-th percentile427
Maximum474
Range412
Interquartile range (IQR)96.935296

Descriptive statistics

Standard deviation80.824691
Coefficient of variation (CV)0.3078736
Kurtosis-0.030523109
Mean262.52557
Median Absolute Deviation (MAD)49.117358
Skewness0.32327866
Sum3321211
Variance6532.6307
MonotonicityNot monotonic
2024-01-03T11:36:32.640956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
445 221
 
1.7%
344 184
 
1.5%
248 166
 
1.3%
228 163
 
1.3%
467 157
 
1.2%
251 151
 
1.2%
238 149
 
1.2%
295 145
 
1.1%
156 138
 
1.1%
224 137
 
1.1%
Other values (5811) 11040
87.3%
ValueCountFrequency (%)
62 3
 
< 0.1%
70 2
 
< 0.1%
71 5
 
< 0.1%
76 1
 
< 0.1%
78.31447354 1
 
< 0.1%
79 13
0.1%
80 3
 
< 0.1%
80.15243046 1
 
< 0.1%
80.28200419 1
 
< 0.1%
81 7
0.1%
ValueCountFrequency (%)
474 10
 
0.1%
471.6235288 1
 
< 0.1%
471 8
 
0.1%
467 157
1.2%
466.8547027 1
 
< 0.1%
466.2533335 1
 
< 0.1%
466.0722616 1
 
< 0.1%
464.1230986 1
 
< 0.1%
464 1
 
< 0.1%
463.761467 1
 
< 0.1%

Prothrombin
Real number (ℝ)

Distinct5818
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.580938
Minimum9
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.0 KiB
2024-01-03T11:36:32.755857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.7
Q110.095267
median10.6
Q311
95-th percentile11.604313
Maximum12.5
Range3.5
Interquartile range (IQR)0.90473261

Descriptive statistics

Standard deviation0.60089942
Coefficient of variation (CV)0.056790753
Kurtosis-0.37264896
Mean10.580938
Median Absolute Deviation (MAD)0.46457089
Skewness0.36708774
Sum133859.44
Variance0.36108012
MonotonicityNot monotonic
2024-01-03T11:36:32.897360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.6 931
 
7.4%
10 819
 
6.5%
11 684
 
5.4%
10.1 451
 
3.6%
9.9 447
 
3.5%
9.8 346
 
2.7%
10.9 325
 
2.6%
10.2 286
 
2.3%
10.7 262
 
2.1%
9.6 258
 
2.0%
Other values (5808) 7842
62.0%
ValueCountFrequency (%)
9 9
 
0.1%
9.044752702 1
 
< 0.1%
9.1 9
 
0.1%
9.2 4
 
< 0.1%
9.3 7
 
0.1%
9.342620491 1
 
< 0.1%
9.4 15
 
0.1%
9.5 124
1.0%
9.507821909 1
 
< 0.1%
9.515172206 1
 
< 0.1%
ValueCountFrequency (%)
12.5 2
 
< 0.1%
12.44917219 1
 
< 0.1%
12.44359226 1
 
< 0.1%
12.40018171 1
 
< 0.1%
12.4 19
0.2%
12.39273078 1
 
< 0.1%
12.38196979 1
 
< 0.1%
12.37284104 1
 
< 0.1%
12.37231458 1
 
< 0.1%
12.37013596 1
 
< 0.1%

Stage
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size741.4 KiB
4.0
5336 
3.0
5143 
2.0
1853 
1.0
 
319

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37953
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 5336
42.2%
3.0 5143
40.7%
2.0 1853
 
14.6%
1.0 319
 
2.5%

Length

2024-01-03T11:36:33.021389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:33.108225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 5336
42.2%
3.0 5143
40.7%
2.0 1853
 
14.6%
1.0 319
 
2.5%

Most occurring characters

ValueCountFrequency (%)
. 12651
33.3%
0 12651
33.3%
4 5336
14.1%
3 5143
13.6%
2 1853
 
4.9%
1 319
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25302
66.7%
Other Punctuation 12651
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12651
50.0%
4 5336
21.1%
3 5143
20.3%
2 1853
 
7.3%
1 319
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 12651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12651
33.3%
0 12651
33.3%
4 5336
14.1%
3 5143
13.6%
2 1853
 
4.9%
1 319
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12651
33.3%
0 12651
33.3%
4 5336
14.1%
3 5143
13.6%
2 1853
 
4.9%
1 319
 
0.8%

Status
Categorical

UNIFORM 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
2
4217 
0
4217 
1
4217 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
2 4217
33.3%
0 4217
33.3%
1 4217
33.3%

Length

2024-01-03T11:36:33.203900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:33.287194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4217
33.3%
0 4217
33.3%
1 4217
33.3%

Most occurring characters

ValueCountFrequency (%)
2 4217
33.3%
0 4217
33.3%
1 4217
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4217
33.3%
0 4217
33.3%
1 4217
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4217
33.3%
0 4217
33.3%
1 4217
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4217
33.3%
0 4217
33.3%
1 4217
33.3%

took_drug
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
1
6431 
0
6220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6431
50.8%
0 6220
49.2%

Length

2024-01-03T11:36:33.380725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:33.456174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6431
50.8%
0 6220
49.2%

Most occurring characters

ValueCountFrequency (%)
1 6431
50.8%
0 6220
49.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6431
50.8%
0 6220
49.2%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6431
50.8%
0 6220
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6431
50.8%
0 6220
49.2%

Edema_N
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
1
12255 
0
 
396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12255
96.9%
0 396
 
3.1%

Length

2024-01-03T11:36:33.541315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:33.705112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 12255
96.9%
0 396
 
3.1%

Most occurring characters

ValueCountFrequency (%)
1 12255
96.9%
0 396
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12255
96.9%
0 396
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12255
96.9%
0 396
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12255
96.9%
0 396
 
3.1%

Edema_S
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
0
12400 
1
 
251

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12400
98.0%
1 251
 
2.0%

Length

2024-01-03T11:36:34.133023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:34.193693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12400
98.0%
1 251
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 12400
98.0%
1 251
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12400
98.0%
1 251
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12400
98.0%
1 251
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12400
98.0%
1 251
 
2.0%

Edema_Y
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.7 KiB
0
12506 
1
 
145

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12651
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12506
98.9%
1 145
 
1.1%

Length

2024-01-03T11:36:34.264739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:36:34.327362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12506
98.9%
1 145
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 12506
98.9%
1 145
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12651
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12506
98.9%
1 145
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12506
98.9%
1 145
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12506
98.9%
1 145
 
1.1%

Interactions

2024-01-03T11:36:27.904676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.106321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.869879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.598207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.808736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.850239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.091906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.537962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.513951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.718965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.703979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:28.020266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.187764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.936414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.663583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.924096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.982603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.180911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.620249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.609823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.807446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.920975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:28.207256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.251827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.996579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.200637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.020196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.078383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.259355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.713210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.709753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.888433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.038167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:28.358750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.320938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.061833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.265596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.134073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.187295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.342349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.787169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.870055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.965516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.127786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:28.509092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.388569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.125467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.331319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.256164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.266829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.420634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.852262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.988152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.042024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.226077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:28.989752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.460614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.192274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.397620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.353378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.342554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.506752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.918552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.080711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.120770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.335867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:29.101196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.533327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.265485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.468809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.440467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.428378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.600528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.034914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.167923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.193904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.427856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:29.186685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.597341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.329425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.535013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.514291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.510336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.686771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.129881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.245454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.260705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.504362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:29.270635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.669142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.398716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.606729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.594435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.627044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.775964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.236211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.406178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.341086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.595128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:29.354869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.740879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.469413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.679297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.674805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.810256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:22.865405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.332733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.524134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.438569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.697525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:29.437450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:17.801447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:18.528430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:19.739372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:20.752709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:21.985791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:23.450014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:24.401245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:25.611554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:26.516445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:36:27.793917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-01-03T11:36:34.386726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AgeAlbuminAlk_PhosAscitesBilirubinCholesterolCopperEdema_NEdema_SEdema_YHepatomegalyN_yearsPlateletsProthrombinSGOTSpidersStageStatusTrygliceridesis_maletook_drug
Age1.000-0.056-0.1420.079-0.061-0.147-0.0600.1140.1020.0690.119-0.055-0.1370.110-0.1230.0580.0960.313-0.0360.1140.076
Albumin-0.0561.000-0.1550.218-0.236-0.022-0.1910.1260.0470.1440.2140.1930.113-0.087-0.1710.1230.1350.158-0.0330.0490.119
Alk_Phos-0.142-0.1551.0000.0330.2990.3130.1960.0840.1120.0710.195-0.1110.1350.0140.4900.2010.1300.1990.1560.1160.261
Ascites0.0790.2180.0331.0000.057-0.0840.0070.2710.0480.3750.063-0.100-0.1110.1180.0040.0860.0740.121-0.0150.0160.000
Bilirubin-0.061-0.2360.2990.0571.0000.3380.5410.0580.0370.0900.417-0.361-0.1090.1940.4380.2880.2080.4240.1780.0660.092
Cholesterol-0.147-0.0220.313-0.0840.3381.0000.2040.0660.0590.0960.127-0.0800.135-0.0590.3390.0990.0810.1850.2690.0730.196
Copper-0.060-0.1910.1960.0070.5410.2041.0000.0730.0530.1130.330-0.317-0.0800.0900.3410.2810.1590.3110.2370.1070.118
Edema_N0.1140.1260.0840.2710.0580.0660.0731.0000.7900.5970.0660.0620.063-0.145-0.0130.1250.0830.1390.0540.0440.036
Edema_S0.1020.0470.1120.0480.0370.0590.0530.7901.0000.0090.026-0.0390.0020.0760.0080.0450.0370.073-0.0340.0470.027
Edema_Y0.0690.1440.0710.3750.0900.0960.1130.5970.0091.0000.072-0.050-0.1060.1380.0120.1430.0900.142-0.0440.0000.021
Hepatomegaly0.1190.2140.1950.0630.4170.1270.3300.0660.0260.0721.000-0.217-0.2600.1760.1940.3020.4970.4010.1510.0080.102
N_years-0.0550.193-0.111-0.100-0.361-0.080-0.3170.062-0.039-0.050-0.2171.0000.130-0.083-0.1950.1860.1580.268-0.1130.0560.071
Platelets-0.1370.1130.135-0.111-0.1090.135-0.0800.0630.002-0.106-0.2600.1301.000-0.205-0.0390.2390.1240.2140.0540.0790.081
Prothrombin0.110-0.0870.0140.1180.194-0.0590.090-0.1450.0760.1380.176-0.083-0.2051.0000.0790.2140.1270.273-0.1100.0610.060
SGOT-0.123-0.1710.4900.0040.4380.3390.341-0.0130.0080.0120.194-0.195-0.0390.0791.0000.1920.1370.3020.0900.0950.123
Spiders0.0580.1230.2010.0860.2880.0990.2810.1250.0450.1430.3020.1860.2390.2140.1921.0000.1990.209-0.0290.0500.028
Stage0.0960.1350.1300.0740.2080.0810.1590.0830.0370.0900.4970.1580.1240.1270.1370.1991.0000.3190.0450.0220.015
Status0.3130.1580.1990.1210.4240.1850.3110.1390.0730.1420.4010.2680.2140.2730.3020.2090.3191.0000.0530.1180.058
Tryglicerides-0.036-0.0330.156-0.0150.1780.2690.2370.054-0.034-0.0440.151-0.1130.054-0.1100.090-0.0290.0450.0531.0000.0570.157
is_male0.1140.0490.1160.0160.0660.0730.1070.0440.0470.0000.0080.0560.0790.0610.0950.0500.0220.1180.0571.0000.000
took_drug0.0760.1190.2610.0000.0920.1960.1180.0360.0270.0210.1020.0710.0810.0600.1230.0280.0150.0580.1570.0001.000

Missing values

2024-01-03T11:36:29.549738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-03T11:36:29.767262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

N_yearsAgeis_maleAscitesHepatomegalySpidersBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatustook_drugEdema_NEdema_SEdema_Y
02.73698658.9917811.00002.3316.03.35172.01601.0179.8063.0394.09.73.021100
17.05205552.7041100.00000.9364.03.5463.01440.0134.8588.0361.011.03.000100
29.39178137.6082190.00113.3299.03.55131.01029.0119.3550.0199.011.74.020001
37.05753450.5753420.00000.6256.03.5058.01653.071.3096.0269.010.73.000100
42.15890445.6383560.00101.1346.03.6563.01181.0125.5596.0298.010.64.000100
53.56164448.5013700.00001.0328.03.3543.01677.0137.9590.0291.09.83.000100
64.42465858.3041100.00100.6273.03.9436.0598.052.70214.0227.09.93.000100
75.61643856.6684930.00000.7360.03.6572.03196.094.55154.0269.09.82.001100
87.16438441.1205480.00000.9478.03.6039.01758.0171.00140.0234.010.62.001100
99.81095970.6082190.00000.5252.03.6026.0377.056.76185.0336.010.02.000100
N_yearsAgeis_maleAscitesHepatomegalySpidersBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatustook_drugEdema_NEdema_SEdema_Y
126414.07412450.1909120.00112.688480224.4607943.578298124.9232011045.407357120.291904104.430720182.59264311.0392324.020100
126426.03096758.4868720.00100.722277258.0000003.91403049.000000559.00000043.400000148.639637279.48812411.0800984.020100
126434.34544347.4167920.00111.653824327.1535963.638445129.537587932.231694150.324485159.770580337.07647310.8461444.021100
126447.29223456.1446770.00111.613946248.8456993.63786346.3827961105.347206102.15725772.124623146.26408511.0676564.021100
126452.47671263.3293790.00103.596415396.0000003.19276058.0000001440.000000153.450000131.000000163.45162510.1379934.021100
126462.27414960.8408960.00112.905185394.1314383.297450158.2509911820.828596132.069302133.932300202.73316111.1565724.021100
126476.00856848.8242120.00103.478799444.2756123.61409945.4841012045.00000089.900000111.332158225.0000009.7120144.021100
126486.77351951.7131100.00112.157310261.8958003.521765123.416801792.25040291.43434791.521001179.90589811.0521004.020100
126497.72454856.8037640.00112.000000267.0000003.07839589.000000754.000000195.28615090.000000140.56598511.8000004.020100
126503.43813048.2625600.00113.071357267.2814163.57450597.2764021160.278073110.05000081.994986200.0000009.8284764.021100

Duplicate rows

Most frequently occurring

N_yearsAgeis_maleAscitesHepatomegalySpidersBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatustook_drugEdema_NEdema_SEdema_Y# duplicates
176.76164447.2136990.00101.3316.03.5175.01162.0147.25137.0238.010.03.01010023
136.13972640.2876710.00000.5201.03.7344.01345.054.25145.0445.010.13.01010017
94.12054838.1315070.00113.4279.03.53143.0671.0113.1572.0136.010.93.0111008
126.13972640.2876710.00000.5201.03.7344.01345.054.25145.0445.010.12.0101006
146.43835641.1808220.00005.5528.04.1877.02404.0172.0578.0467.010.73.0111006
186.76164447.2136990.00101.3316.03.5175.01162.0147.25137.0238.010.04.0101006
196.78082236.5178080.00003.4450.03.3732.01408.0116.25118.0313.011.23.0111006
32.46849340.9287670.00103.2339.03.18123.03336.0205.0084.0304.09.94.0111004
42.47671261.3369860.00103.9396.03.2058.01440.0153.45131.0156.010.04.0211004
11.46027456.0246580.00111.2275.03.43100.01142.075.0091.0217.011.33.0111003